






"""simple docstring"""





import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
      MODEL_FOR_QUESTION_ANSWERING_MAPPING,
      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
      MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
      LayoutLMvaConfig,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaModel,
  )
  from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
  from PIL import Image

  from transformers import LayoutLMvaImageProcessor






class 	a		:






    def __init__(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				str=2			,		__SCREAMING_SNAKE_CASE	:				List[str]=3			,		__SCREAMING_SNAKE_CASE	:				str=4			,		__SCREAMING_SNAKE_CASE	:				Any=2			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=7			,		__SCREAMING_SNAKE_CASE	:				Tuple=True			,		__SCREAMING_SNAKE_CASE	:				int=True			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=True			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=True			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=99			,		__SCREAMING_SNAKE_CASE	:				int=36			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=3			,		__SCREAMING_SNAKE_CASE	:				List[Any]=4			,		__SCREAMING_SNAKE_CASE	:				List[str]=37			,		__SCREAMING_SNAKE_CASE	:				Tuple="gelu"			,		__SCREAMING_SNAKE_CASE	:				List[Any]=0.1			,		__SCREAMING_SNAKE_CASE	:				List[str]=0.1			,		__SCREAMING_SNAKE_CASE	:				List[Any]=512			,		__SCREAMING_SNAKE_CASE	:				List[Any]=16			,		__SCREAMING_SNAKE_CASE	:				str=2			,		__SCREAMING_SNAKE_CASE	:				List[Any]=0.02			,		__SCREAMING_SNAKE_CASE	:				List[str]=6			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=6			,		__SCREAMING_SNAKE_CASE	:				Dict=3			,		__SCREAMING_SNAKE_CASE	:				int=4			,		__SCREAMING_SNAKE_CASE	:				Any=None			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=1000			,		)	->						Dict:
      lowerCamelCase_       	=		parent
      lowerCamelCase_       	=		batch_size
      lowerCamelCase_       	=		num_channels
      lowerCamelCase_       	=		image_size
      lowerCamelCase_       	=		patch_size
      lowerCamelCase_       	=		text_seq_length
      lowerCamelCase_       	=		is_training
      lowerCamelCase_       	=		use_input_mask
      lowerCamelCase_       	=		use_token_type_ids
      lowerCamelCase_       	=		use_labels
      lowerCamelCase_       	=		vocab_size
      lowerCamelCase_       	=		hidden_size
      lowerCamelCase_       	=		num_hidden_layers
      lowerCamelCase_       	=		num_attention_heads
      lowerCamelCase_       	=		intermediate_size
      lowerCamelCase_       	=		hidden_act
      lowerCamelCase_       	=		hidden_dropout_prob
      lowerCamelCase_       	=		attention_probs_dropout_prob
      lowerCamelCase_       	=		max_position_embeddings
      lowerCamelCase_       	=		type_vocab_size
      lowerCamelCase_       	=		type_sequence_label_size
      lowerCamelCase_       	=		initializer_range
      lowerCamelCase_       	=		coordinate_size
      lowerCamelCase_       	=		shape_size
      lowerCamelCase_       	=		num_labels
      lowerCamelCase_       	=		num_choices
      lowerCamelCase_       	=		scope
      lowerCamelCase_       	=		range_bbox

      # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
      lowerCamelCase_       	=		text_seq_length
      lowerCamelCase_       	=		(image_size // patch_size) ** 2 + 1
      lowerCamelCase_       	=		self.text_seq_length + self.image_seq_length






    def   UpperCamelCase			(			self	:				List[Any]   )	->						Optional[Any]:
      lowerCamelCase_       	=		ids_tensor([self.batch_size, self.text_seq_length]			,		self.vocab_size   )

      lowerCamelCase_       	=		ids_tensor([self.batch_size, self.text_seq_length, 4]			,		self.range_bbox   )
      # Ensure that bbox is legal
      for i in range(bbox.shape[0]   ):
        for j in range(bbox.shape[1]   ):
          if bbox[i, j, 3] < bbox[i, j, 1]:
            lowerCamelCase_       	=		bbox[i, j, 3]
            lowerCamelCase_       	=		bbox[i, j, 1]
            lowerCamelCase_       	=		t
          if bbox[i, j, 2] < bbox[i, j, 0]:
            lowerCamelCase_       	=		bbox[i, j, 2]
            lowerCamelCase_       	=		bbox[i, j, 0]
            lowerCamelCase_       	=		t

      lowerCamelCase_       	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

      lowerCamelCase_       	=		None
      if self.use_input_mask:
        lowerCamelCase_       	=		random_attention_mask([self.batch_size, self.text_seq_length]   )

      lowerCamelCase_       	=		None
      if self.use_token_type_ids:
        lowerCamelCase_       	=		ids_tensor([self.batch_size, self.text_seq_length]			,		self.type_vocab_size   )

      lowerCamelCase_       	=		None
      lowerCamelCase_       	=		None
      if self.use_labels:
        lowerCamelCase_       	=		ids_tensor([self.batch_size]			,		self.type_sequence_label_size   )
        lowerCamelCase_       	=		ids_tensor([self.batch_size, self.text_seq_length]			,		self.num_labels   )

      lowerCamelCase_       	=		LayoutLMvaConfig(
          vocab_size=self.vocab_size			,		hidden_size=self.hidden_size			,		num_hidden_layers=self.num_hidden_layers			,		num_attention_heads=self.num_attention_heads			,		intermediate_size=self.intermediate_size			,		hidden_act=self.hidden_act			,		hidden_dropout_prob=self.hidden_dropout_prob			,		attention_probs_dropout_prob=self.attention_probs_dropout_prob			,		max_position_embeddings=self.max_position_embeddings			,		type_vocab_size=self.type_vocab_size			,		initializer_range=self.initializer_range			,		coordinate_size=self.coordinate_size			,		shape_size=self.shape_size			,		input_size=self.image_size			,		patch_size=self.patch_size			,		)

      return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels






    def   UpperCamelCase			(			self	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Dict			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]   )	->						Any:
      lowerCamelCase_       	=		LayoutLMvaModel(config=__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()

      # text + image
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE			,		pixel_values=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		model(
          __SCREAMING_SNAKE_CASE			,		bbox=__SCREAMING_SNAKE_CASE			,		pixel_values=__SCREAMING_SNAKE_CASE			,		attention_mask=__SCREAMING_SNAKE_CASE			,		token_type_ids=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE			,		bbox=__SCREAMING_SNAKE_CASE			,		pixel_values=__SCREAMING_SNAKE_CASE			,		token_type_ids=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE			,		bbox=__SCREAMING_SNAKE_CASE			,		pixel_values=__SCREAMING_SNAKE_CASE   )

      self.parent.assertEqual(result.last_hidden_state.shape			,		(self.batch_size, self.seq_length, self.hidden_size)   )

      # text only
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE   )
      self.parent.assertEqual(
          result.last_hidden_state.shape			,		(self.batch_size, self.text_seq_length, self.hidden_size)   )

      # image only
      lowerCamelCase_       	=		model(pixel_values=__SCREAMING_SNAKE_CASE   )
      self.parent.assertEqual(
          result.last_hidden_state.shape			,		(self.batch_size, self.image_seq_length, self.hidden_size)   )






    def   UpperCamelCase			(			self	:				Any			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				List[str]   )	->						List[str]:
      lowerCamelCase_       	=		self.num_labels
      lowerCamelCase_       	=		LayoutLMvaForSequenceClassification(__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()
      lowerCamelCase_       	=		model(
          __SCREAMING_SNAKE_CASE			,		bbox=__SCREAMING_SNAKE_CASE			,		pixel_values=__SCREAMING_SNAKE_CASE			,		attention_mask=__SCREAMING_SNAKE_CASE			,		token_type_ids=__SCREAMING_SNAKE_CASE			,		labels=__SCREAMING_SNAKE_CASE			,		)
      self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.num_labels)   )






    def   UpperCamelCase			(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				List[str]   )	->						int:
      lowerCamelCase_       	=		self.num_labels
      lowerCamelCase_       	=		LayoutLMvaForTokenClassification(config=__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()
      lowerCamelCase_       	=		model(
          __SCREAMING_SNAKE_CASE			,		bbox=__SCREAMING_SNAKE_CASE			,		pixel_values=__SCREAMING_SNAKE_CASE			,		attention_mask=__SCREAMING_SNAKE_CASE			,		token_type_ids=__SCREAMING_SNAKE_CASE			,		labels=__SCREAMING_SNAKE_CASE			,		)
      self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.text_seq_length, self.num_labels)   )






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				List[str]   )	->						Union[str, Any]:
      lowerCamelCase_       	=		LayoutLMvaForQuestionAnswering(config=__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()
      lowerCamelCase_       	=		model(
          __SCREAMING_SNAKE_CASE			,		bbox=__SCREAMING_SNAKE_CASE			,		pixel_values=__SCREAMING_SNAKE_CASE			,		attention_mask=__SCREAMING_SNAKE_CASE			,		token_type_ids=__SCREAMING_SNAKE_CASE			,		start_positions=__SCREAMING_SNAKE_CASE			,		end_positions=__SCREAMING_SNAKE_CASE			,		)
      self.parent.assertEqual(result.start_logits.shape			,		(self.batch_size, self.seq_length)   )
      self.parent.assertEqual(result.end_logits.shape			,		(self.batch_size, self.seq_length)   )






    def   UpperCamelCase			(			self	:				Any   )	->						Optional[Any]:
      lowerCamelCase_       	=		self.prepare_config_and_inputs()
      (
          (
          lowerCamelCase_
      )	,      (
          lowerCamelCase_
      )	,      (
          lowerCamelCase_
      )	,      (
          lowerCamelCase_
      )	,      (
          lowerCamelCase_
      )	,      (
          lowerCamelCase_
      )	,      (
          lowerCamelCase_
      )	,      (
          lowerCamelCase_
      )	,      
      )       	=		config_and_inputs
      lowerCamelCase_       	=		{
          'input_ids': input_ids,
          'bbox': bbox,
          'pixel_values': pixel_values,
          'token_type_ids': token_type_ids,
          'attention_mask': input_mask,
      }
      return config, inputs_dict






@require_torch
class 	a		(   __snake_case    , __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	int		=					False
    SCREAMING_SNAKE_CASE		:	Any		=					False
    SCREAMING_SNAKE_CASE		:	Union[str, Any]		=					False

    SCREAMING_SNAKE_CASE		:	List[str]		=					(
        (
            LayoutLMvaModel,
            LayoutLMvaForSequenceClassification,
            LayoutLMvaForTokenClassification,
            LayoutLMvaForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    SCREAMING_SNAKE_CASE		:	Any		=					(
        {"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
        if is_torch_available()
        else {}
    )






    def   UpperCamelCase			(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]   )	->						Union[str, Any]:
      # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
      # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
      # the sequence dimension of the text embedding only.
      # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
      return True






    def   UpperCamelCase			(			self	:				List[Any]   )	->						Tuple:
      lowerCamelCase_       	=		LayoutLMvaModelTester(self   )
      lowerCamelCase_       	=		ConfigTester(self			,		config_class=__SCREAMING_SNAKE_CASE			,		hidden_size=37   )






    def   UpperCamelCase			(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				List[str]=False   )	->						List[Any]:
      lowerCamelCase_       	=		copy.deepcopy(__SCREAMING_SNAKE_CASE   )
      if model_class in get_values(__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		{
            k: v.unsqueeze(1   ).expand(-1			,		self.model_tester.num_choices			,		-1   ).contiguous()
            if isinstance(__SCREAMING_SNAKE_CASE			,		torch.Tensor   ) and v.ndim > 1
            else v
            for k, v in inputs_dict.items()
        }
      if return_labels:
        if model_class in get_values(__SCREAMING_SNAKE_CASE   ):
          lowerCamelCase_       	=		torch.ones(self.model_tester.batch_size			,		dtype=torch.long			,		device=__SCREAMING_SNAKE_CASE   )
        elif model_class in get_values(__SCREAMING_SNAKE_CASE   ):
          lowerCamelCase_       	=		torch.zeros(
              self.model_tester.batch_size			,		dtype=torch.long			,		device=__SCREAMING_SNAKE_CASE   )
          lowerCamelCase_       	=		torch.zeros(
              self.model_tester.batch_size			,		dtype=torch.long			,		device=__SCREAMING_SNAKE_CASE   )
        elif model_class in [
            *get_values(__SCREAMING_SNAKE_CASE   ),
        ]:
          lowerCamelCase_       	=		torch.zeros(
              self.model_tester.batch_size			,		dtype=torch.long			,		device=__SCREAMING_SNAKE_CASE   )
        elif model_class in [
            *get_values(__SCREAMING_SNAKE_CASE   ),
        ]:
          lowerCamelCase_       	=		torch.zeros(
              (self.model_tester.batch_size, self.model_tester.text_seq_length)			,		dtype=torch.long			,		device=__SCREAMING_SNAKE_CASE			,		)

      return inputs_dict






    def   UpperCamelCase			(			self	:				List[Any]   )	->						Union[str, Any]:
      self.config_tester.run_common_tests()






    def   UpperCamelCase			(			self	:				Dict   )	->						int:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Dict   )	->						List[Any]:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
        lowerCamelCase_       	=		type
        self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				str   )	->						str:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				str   )	->						int:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Dict   )	->						Optional[Any]:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE   )






    @slow
    def   UpperCamelCase			(			self	:				List[str]   )	->						Union[str, Any]:
      for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCamelCase_       	=		LayoutLMvaModel.from_pretrained(__SCREAMING_SNAKE_CASE   )
        self.assertIsNotNone(__SCREAMING_SNAKE_CASE   )







def 	lowerCamelCase__			(				) ->							Any:
  lowerCamelCase_       	=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
  return image






@require_torch
class 	a		(   unittest.TestCase						):






    @cached_property
    def   UpperCamelCase			(			self	:				Dict   )	->						Any:
      return LayoutLMvaImageProcessor(apply_ocr=__SCREAMING_SNAKE_CASE   ) if is_vision_available() else None






    @slow
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						List[str]:
      lowerCamelCase_       	=		LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'   ).to(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		self.default_image_processor
      lowerCamelCase_       	=		prepare_img()
      lowerCamelCase_       	=		image_processor(images=__SCREAMING_SNAKE_CASE			,		return_tensors='pt'   ).pixel_values.to(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.tensor([[1, 2]]   )
      lowerCamelCase_       	=		torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]   ).unsqueeze(0   )

      # forward pass
      lowerCamelCase_       	=		model(
          input_ids=input_ids.to(__SCREAMING_SNAKE_CASE   )			,		bbox=bbox.to(__SCREAMING_SNAKE_CASE   )			,		pixel_values=pixel_values.to(__SCREAMING_SNAKE_CASE   )			,		)

      # verify the logits
      lowerCamelCase_       	=		torch.Size((1, 199, 768)   )
      self.assertEqual(outputs.last_hidden_state.shape			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.tensor(
          [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]   ).to(__SCREAMING_SNAKE_CASE   )

      self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]			,		__SCREAMING_SNAKE_CASE			,		atol=1e-4   )   )












"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						float						,							_lowerCamelCase			:						float					) ->							float:

  if density <= 0:
    raise ValueError('Impossible fluid density'					)
  if bulk_modulus <= 0:
    raise ValueError('Impossible bulk modulus'					)

  return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
  import doctest

  doctest.testmod()




